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Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
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In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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Representation learning of natural language and its application to language understanding and generation
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Neural-based Knowledge Transfer in Natural Language Processing
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Impact of textual data augmentation on linguistic pattern extraction to improve the idiomaticity of extractive summaries
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In: Lecture Notes in Computer Science ; https://hal.archives-ouvertes.fr/hal-03271380 ; Matteo Golfarelli; Robert Wrembel. Lecture Notes in Computer Science, Springer, In press, Lecture Notes in Computer Science (2021)
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The Mapping of Deep Language Models on Brain Responses Primarily Depends on their Performance
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In: https://hal.archives-ouvertes.fr/hal-03361439 ; 2021 (2021)
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A logistic regression model for predicting child language performance ; Un modèle de régression logistique pour la prédiction du développement langagier chez l'enfant
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In: SIS 2021, 50th Annuale Conference of the Italian Statistical Society" ; https://hal.archives-ouvertes.fr/hal-03318721 ; SIS 2021, 50th Annuale Conference of the Italian Statistical Society", Jun 2021, Pise, Italy (2021)
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Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
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In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
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Multilingual Email Zoning - Segmenting Multilingual Email Text Into Zones
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Contextualised sentiment analysis in the financial domain
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Abstract:
Sentiments and beliefs play an important role in actions and decisions in a market environment; for example, people disliking a brand will tend to avoid products of it or people aiming to reduce their carbon-dioxide footprint will tend to travel less. While these choices and their subsequent actions are driven by sentiment, they all have an economic effect. In our connected world, where society, economy, politics, and many others are linked to one another, one must consider the sentiments emanating from different parties (e.g. The Wall Street Journal, Twitter, analyst reports, and company reports). To determine a market sentiment, it is vital to analyse sentiments while considering their differences to ensure the consideration of different interpretations, expressions, and entity levels targeted by sentiment. Driven by the emergence of the Internet as a means to share data and the invention of social platforms, an increasing amount of text data is produced every day, with a broader and more diverse set of authors. Furthermore, the Internet increased the availability of trading opportunities and allowed easier access to the markets by the general public. Layman, as well as journalists, politicians, and economists, can participate in the markets and publish their statements and opinions on the Internet via text. Online textual data sources provide a rich opportunity to harvest information about the public and are a prime target to analyse sentiments. State-of-the-art Financial Sentiment Analysis approaches base their analysis only on a given text which comes with two bottlenecks. First, it makes the detection of implicit sentiments difficult and, second, the role of sentiment contagion is not considered. This thesis presents methods dealing with the mentioned bottlenecks by employing a contextual approach to Sentiment Analysis. We make the following contributions to Sentiment Analysis: First, we define the three novel concepts of sentiment conveyance, linking, and assigning, necessary to model textual sentiment contagion. Second, we investigate whether different data sources can be used to enhance Sentiment Analysis on each other. Third, we introduce the first graph neural network for Financial Sentiment Analysis using text and relationship features based on temporal, word, and entity information. Finally, we present a novel corpus (FinLin), tailored to contextual Financial Sentiment Analysis, which covers four data types from the same period and targets a selection of entities from the automobile sector. ; 2022-01-05
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Keyword:
Computer Science; Contextual Sentiment Analysis; Deep Learning; Finance; Graph; Implicit Sentiments; Informatics; Machine Learning; Natural Language Processing; Science and Engineering; Sentiment Contagion
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URL: http://hdl.handle.net/10379/16852
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Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction ...
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DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction ...
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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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